</td></tr><tr><td>Inverted-classroom Approaches to Educate Undergraduate and High-school Students MATLAB in Process Control and Modeling</td><td nowrap>

to:

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M04ZVPX5G1SBY7QZ65GDGLQY4L-18BV'>Inverted-classroom Approaches to Educate Undergraduate and High-school Students MATLAB in Process Control and Modeling</a></td><td nowrap>

</td></tr><tr><td>Inverted-classroom Approaches to Educate Undergraduate and High-school Students MATLAB in Process Control and Modeling</td><td nowrap>
May 9<br>10 AM Eastern
</td><td nowrap>Zuyi (Jacky) Huang
</td><td>Villanova University

To participate in an upcoming presentation, click the link above to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

To participate in an upcoming presentation, click the link above to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

Webinars consist of applications and tutorials in mathematical modeling, estimation, and optimization. The sessions are hosted with WebEx with interactive chat, video conferencing, and remote computer desktop sharing. Each of the sessions is recorded and later posted online. Sessions are sponsored by the Computing and Systems Technology (CAST) division of the American Institute of Chemical Engineers (AIChE) as the WebCAST series.

to:

Webinars consist of applications and tutorials in mathematical modeling, estimation, and optimization. The sessions are hosted with WebEx with interactive chat, video conferencing, and remote computer desktop sharing. Each of the sessions is recorded and later posted online. Sessions are sponsored by the Computing and Systems Technology (CAST) division of the American Institute of Chemical Engineers (AIChE) as the WebCAST series.

Webinars consist of applications and tutorials in mathematical modeling, estimation, and optimization. The sessions are hosted with WebEx with interactive chat, video conferencing, and remote computer desktop sharing. Each of the sessions is recorded and later posted online.

to:

Webinars consist of applications and tutorials in mathematical modeling, estimation, and optimization. The sessions are hosted with WebEx with interactive chat, video conferencing, and remote computer desktop sharing. Each of the sessions is recorded and later posted online. Sessions are sponsored by the Computing and Systems Technology (CAST) division of the American Institute of Chemical Engineers (AIChE) as the WebCAST series.

</td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations.

</td><td>Juan Ruiz, Visual MESA
</td><td>Even though there has been a significant increase in the use of MIP models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP).

to:

</td><td>Juan Ruiz
</td><td>Visual MESA

Changed lines 273-275 from:

</td><td>Kody Powell, UT Austin
</td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant requires the use of conditional arguments that change the system dynamics. New formulations of <a href='https://apmonitor.com/wiki/index.php/Apps/MpecExamples'>Mathematical Programs with Equilibrium Constraints (MPECs)</a> have improved convergence properties to enable more realistic dynamic simulations.

to:

</td><td>Kody Powell
</td><td>UT Austin

Changed lines 281-283 from:

</td><td>Todd Barber, NASA JPL
</td><td>The Curiosity robot is equipped with a nuclear-powered lab capable of vaporizing rocks and ingesting soil, measuring habitability, and potentially paving the way for human exploration. Todd will review interesting and unusual experiences in his career as a NASA engineer. He will present audio-visual highlights and recent findings of the Mars Rover Missions. (Spirit, Opportunity, and Curiosity).

to:

</td><td>Todd Barber
</td><td>NASA JPL

Changed lines 288-290 from:

</td><td>Liang Sun, BYU
</td><td>Moving Horizon Estimation (MHE) is used to update a mathematical model during real-time flight tests. Results from the flight tests demonstrate that large-scale models can be applied in real-time applications for estimation and control.

to:

</td><td>Liang Sun
</td><td>BYU

Changed lines 295-296 from:

</td><td>The equations that simulate separation processes naturally involve higher index Differential and Algebraic Equations (DAEs). Other popular techniques for DAE simulation can often only handle up to index-1 DAEs. New techniques for large-scale DAEs allow efficient solution for bifurcation analysis, simulation of complex systems, and stability analysis.

to:

</td><td>BASF

Changed lines 301-303 from:

</td><td>Prediction of protein structures is a rapidly advancing area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems.

</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/wiki/index.php/Main/SBML'>APM conversion utility</a> and solving SBML example problems.

to:

</td><td>BYU.

Changed lines 318-321 from:

</td><td>Trevor Slade and Reza Asgharzadeh
</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

</td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers. <a href='/wiki/uploads/Main/apm_mpc_demo.zip'>Download example application</a>

to:

</td><td>BYU

Changed lines 331-332 from:

</td><td>To identify models for MPC applications in chemical plants and refineries, the process is perturbed through increasingly automated step testing. Mark will give a tutorial overview of how to apply practical control engineering and design experiments for model development of dynamic systems.

to:

</td><td>CMiD Solutions

Changed lines 336-339 from:

</td><td>Reza Asgharzadeh, Chris Lyden, and Jim Huff
</td><td>Thermal oxidizers are used in chemical plants and refineries to combust waste streams with low concentrations of reactants. The design and operation of the thermal oxidizer is of crucial importance for safety, environmental, and economic reasons.

</td><td>The APM interface extends MATLAB to be used for parameter estimation, nonlinear control, and optimization. Participants can <a href='/wiki/uploads/Main/apm_demo.zip'>download a few example applications</a> to run prior to the meeting that will be covered as part of the tutorial.

</td><td>The APM interface extends Python to be used a variety of optimization applications. Dynamic optimization programming applications are demonstrated with Python.

to:

</td><td>BYU

Changed lines 355-356 from:

</td><td>Solid Oxide Fuel Cells (SOFCs) can be damaged by load changes that shorten life through delamination and micro-cracking. Application of SOFCs in load following applications is discussed to improve lifecycle costs.

to:

</td><td>BYU

Changed lines 361-362 from:

</td><td>Path optimization of an Unmanned Aerial Vehicle (UAV) becomes more challenging with select constraints. Application of a UAV application is discussed from the MAGICC Lab group at BYU.

to:

</td><td>BYU

Changed lines 366-368 from:

</td><td>Kody Powell, UT Austin
</td><td>Dynamic thermal energy storage with weather forecasting has the potential to improve solar energy capture by up to 64%. Results from a recent study are discussed to demonstrate the benefits of dynamic optimization.

to:

</td><td>Kody Powell
</td><td>UT Austin

Changed lines 373-374 from:

</td><td>Large scale biological models are important to gain an understanding of complex pathways to improve treatments such as anti-viral application. Simulated clinical trial data is aligned to an available model of HIV infection.

to:

</td><td>BYU

Changed line 379 from:

</td><td>Friction Stir Welding (FSW) temperature control is important to achieve uniform and consistent weld properties. This application is using a PDAE model for estimation and control. The model validation, control, and experimental data testing are discussed.

</td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations. These techniques will allow the industry to gain greater insight and understanding of how natural fracture geometry and intensity, fracture hydromechanical properties, and stress field affect hydraulic fracturing performance in the presence of different operational parameters.

to:

</td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations.

</td><td>Mixed-Integer Programming (MIP) provides a framework for mathematically modeling many optimization problems that involve discrete and continuous variables. Even though there has been a significant increase in the use of these models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP), a modeling framework that aims at tackling the above question.

to:

</td><td>Even though there has been a significant increase in the use of MIP models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP).

</td><td>Prediction of protein structures is a rapidly advancing area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems. Methods to align experimental results with simulation are discussed.

to:

</td><td>Prediction of protein structures is a rapidly advancing area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems.

To participate in an upcoming presentation, click the link above to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

5 Steps to Start a Technical Presentation

Deleted lines 122-123:

To participate in an upcoming presentation, click the link above to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

Webinars are held about every two-weeks. These seminars consist of applications and tutorials in mathematical modeling, estimation, and optimization.

to:

(:keywords webinar, modeling, optimization, technical:)
(:description Browse sessions for the Symposium on Modeling and Optimization, an online forum for sharing the latest applications and technological developments in process systems engineering.:)

Webinars consist of applications and tutorials in mathematical modeling, estimation, and optimization. The sessions are hosted with WebEx with interactive chat, video conferencing, and remote computer desktop sharing. Each of the sessions is recorded and later posted online.

To participate in an upcoming presentation, click the link to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

to:

To participate in an upcoming presentation, click the link above to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

</td></tr><tr><td>Stimulation Optimization of Unconventional Resources
</td><td>Jan 18, 2013
</td><td nowrap>1 PM MST<br>2 PM CST
</td><td>Marisela Sanchez-Nagel
</td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations. These techniques will allow the industry to gain greater insight and understanding of how natural fracture geometry and intensity, fracture hydromechanical properties, and stress field affect hydraulic fracturing performance in the presence of different operational parameters.

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</td></tr><tr><td>Stimulation Optimization of Unconventional Resources
</td><td><a href='https://youtu.be/_7rHP9-mPj0'>Presentation (55 min)</a>
</td><td>Jan 18, 2013
</td><td>Marisela Sanchez-Nagel
</td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations. These techniques will allow the industry to gain greater insight and understanding of how natural fracture geometry and intensity, fracture hydromechanical properties, and stress field affect hydraulic fracturing performance in the presence of different operational parameters.

</td></tr><tr><td>Stimulation Optimization of Unconventional Resources
</td><td><a href='https://byu.webex.com/byu/j.php?ED=216472567&UID=0&PW=NNDA0NDljNDcw&RT=MiM2'>Join Webinar<br><br>Password:<br> <b>apm2012</b></a>
</td><td>Jan 18, 2013
</td><td nowrap>1 PM MST<br>2 PM CST
</td><td>Marisela Sanchez-Nagel
</td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations. These techniques will allow the industry to gain greater insight and understanding of how natural fracture geometry and intensity, fracture hydromechanical properties, and stress field affect hydraulic fracturing performance in the presence of different operational parameters.

</td><td>Applications of adaptive model reduction in control of distributed processes (processes modeled by PDEs). We will explore the concept of adaptive model reduction, the applications, and advantages.

to:

</td><td>The seminar will cover the concepts and applications of adaptive model reduction in control of distributed processes (processes modeled by PDEs).

</td><td>Sivakumar Pitchaiah, MEMC Electronic Materials
</td><td>Applications of adaptive model reduction in control of distributed processes (processes modeled by PDEs). We will explore the concept of adaptive model reduction, the applications, and advantages.<br>MEMC is a global leader in semiconductor and solar technology. MEMC has been a pioneer in the design and development of silicon wafer technologies for over 50 years.

to:

</td><td>Sivakumar Pitchaiah
</td><td>Applications of adaptive model reduction in control of distributed processes (processes modeled by PDEs). We will explore the concept of adaptive model reduction, the applications, and advantages.

</td><td>Applications of adaptive model reduction in control of distributed processes (processes modeled by PDEs). We will explore the concept of adaptive model reduction, the applications, and advantages.<br>MEMC is a global leader in semiconductor and solar technology. MEMC has been a pioneer in the design and development of silicon wafer technologies for over 50 years.

To participate in an upcoming presentation, click register and provide your name and e-mail address. Registration is used to communicate log-on information and a reminder notice just prior to the meeting. There is no charge to register for a webinar. Users can participate via audio or video conference through a telephone or internet connection.

to:

To participate in an upcoming presentation, click the link to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

</td><td>Surrogate models, by constructing simpler functional approximations of complex and computationally expensive models, allows the use of traditional optimization algorithms for optimization of these systems. This talk discusses three possible algorithms for efficient surrogate model building.

to:

</td><td>Surrogate models, by constructing simpler functional approximations of complex and computationally expensive models, allow the use of traditional optimization algorithms for optimization. This talk discusses three possible algorithms for efficient surrogate model building.

</td><td>John Eason and Selen Cremaschi<br>Univ of Tulsa
</td><td>Surrogate models, by constructing simpler functional approximations of complex and computationally expensive models, allows the use of traditional optimization algorithms for optimization of these systems. This talk discusses three possible algorithms for efficient surrogate model building.

</td></tr><tr><td>Generalized Disjunctive Programming
</td><td><a href='https://byu.webex.com/byu/j.php?ED=210359857&RG=1&UID=0&RT=MiM2'>Register</a>
</td><td>Sept. 18, 2012
</td><td nowrap>9AM MST
</td><td>Juan Ruiz, Visual MESA
</td><td>Mixed-Integer Programming (MIP) provides a framework for mathematically modeling many optimization problems that involve discrete and continuous variables. Even though there has been a significant increase in the use of these models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP), a modeling framework that aims at tackling the above question.

</td></tr><tr><td>Generalized Disjunctive Programming
</td><td><a href='https://youtu.be/dfgzaVd8gLg'>Presentation Video</a>
</td><td>Sept. 18, 2012
</td><td>Juan Ruiz, Visual MESA
</td><td>Mixed-Integer Programming (MIP) provides a framework for mathematically modeling many optimization problems that involve discrete and continuous variables. Even though there has been a significant increase in the use of these models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP), a modeling framework that aims at tackling the above question.

</td><td>Kody Powell, UT Austin
</td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant requires the use of conditional arguments that change the system dynamics. New formulations of <a href='https://apmonitor.com/wiki/index.php/Apps/MpecExamples'>Mathematical Programs with Equilibrium Constraints (MPECs)</a> have improved convergence properties to enable more realistic dynamic simulations.

</td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant has the potential to reduce emissions equivalent to that of 800 cars annually. This green energy project seeks efficiency improvements by more tightly integrating this complex system to achieve peak efficiency.

to:

</td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant requires the use of conditional arguments that change the system dynamics. New formulations of <a href='https://apmonitor.com/wiki/index.php/Apps/MpecExamples'>Mathematical Programs with Equilibrium Constraints (MPECs)</a> have improved convergence properties to enable more realistic dynamic simulations.

</td></tr><tr><td>TBD
</td><td>Webinar
</td><td>Dec 11, 2012
</td><td nowrap>9 AM
</td><td>Sivakumar Pitchaiah, MEMC Electronic Materials
</td><td>MEMC is a global leader in semiconductor and solar technology. MEMC has been a pioneer in the design and development of silicon wafer technologies for over 50 years.

</td><td>Mixed-Integer Programming (MIP) provides a framework for mathematically modeling many optimization problems that involve discrete and continuous variables. Even though there has been a significant increase in the use of these models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP), a modeling framework that aims at tackling the above question.

Webinars are held about every two-weeks at 9 AM Mountain Time / 10 AM Central Time (USA). These seminars consist of applications and tutorials in mathematical modeling, estimation, and optimization applications.

to:

Webinars are held about every two-weeks at 9 AM Mountain Time / 10 AM Central Time (USA). These seminars consist of applications and tutorials in mathematical modeling, estimation, and optimization.

Webinars on Optimization Applications

APM User's Group Webinars are held about every two-weeks at 9 AM Mountain Time / 10 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

to:

Webinars on Modeling and Optimization

Webinars are held about every two-weeks at 9 AM Mountain Time / 10 AM Central Time (USA). These seminars consist of applications and tutorials in mathematical modeling, estimation, and optimization applications.

</td><td nowrap>Jose Mojica<br>Reza Asgharzadeh<br>Thomas Knotts<br>Josh Price
</td><td>Prediction of protein structures is a rapidly advancing area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems. Methods to align experimental results with simulation are discussed.

</td></tr><tr><td>Application of Model Predictive Control
</td><td>
</td><td>June 12, 2012
</td><td nowrap>6AM MST
</td><td>Isak Nielsen
</td><td>Empirical models obtained through subspace identification are a popular form for model predictive control (MPC) applications. This presentation relates a practical application of linear MPC with models obtained by regression techniques. Methods for sparse discrete or continuous Linear Time Invariant (LTI) forms in APMonitor are also reviewed.

</td><td>Predicting protein structures is a critical area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems.

to:

</td><td>Prediction of protein structures is a rapidly advancing area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems. Methods to align experimental results with simulation are discussed.

APM User's Group Webinars are held about every two-weeks at 10 AM Mountain Time / 11 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

to:

APM User's Group Webinars are held about every two-weeks at 9 AM Mountain Time / 10 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/wiki/index.php/Main/SBML'>conversion utility</a> and solving SBML example problems.

to:

</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/wiki/index.php/Main/SBML'>APM conversion utility</a> and solving SBML example problems.

</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/index.php/Main/SBML'>conversion utility</a> and solving SBML example problems.

to:

</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/wiki/index.php/Main/SBML'>conversion utility</a> and solving SBML example problems.

</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the conversion utility and solving SBML example problems.

to:

</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/index.php/Main/SBML'>conversion utility</a> and solving SBML example problems.

</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the conversion utility and solving SBML example problems.

</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is a repository of hundreds of dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization.

</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is a repository of hundreds of dynamic models in computational biology. This presentation will provide a tutorial on using SBML models for dynamic simulation, parameter estimation, and optimization.

to:

</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is a repository of hundreds of dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization.

</td><td>Trevor Slade and Reza Asgharzadeh
</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

</td></tr><tr><td>Fast Model Predictive Control
</td><td><a href='/wiki/uploads/Main/apm_fast_mpc_3Apr12.pdf'>Presentation</a>
</td><td>April 3, 2012
</td><td>Trevor Slade and Reza Asgharzadeh
</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

Added lines 147-194:

</td></tr><tr><td>Optimal Identification for Model Predictive Control
</td><td><a href='/wiki/uploads/Main/apm_darby_13Mar12.pdf'>Presentation</a>
</td><td>March 13, 2012
</td><td>Mark Darby
</td><td>To identify models for MPC applications in chemical plants and refineries, the process is perturbed through increasingly automated step testing. Mark will give a tutorial overview of how to apply practical control engineering and design experiments for model development of dynamic systems.

</td></tr><tr><td>Nonlinear Programming with APM MATLAB
</td><td><a href='/wiki/uploads/Main/apm_tutorial_21Feb12.pdf'>Presentation</a>
</td><td>February 21, 2012
</td><td>John Hedengren
</td><td>The APM interface extends MATLAB to be used for parameter estimation, nonlinear control, and optimization. Participants can <a href='/wiki/uploads/Main/apm_demo.zip'>download a few example applications</a> to run prior to the meeting that will be covered as part of the tutorial.

</td></tr><tr><td>Solid Oxide Fuel Cell Modeling and Control
</td><td><a href='/wiki/uploads/Main/apm_sofc_24Jan12.pdf'>Presentation</a>
</td><td>January 24, 2012
</td><td>Lee Jacobsen
</td><td>Solid Oxide Fuel Cells (SOFCs) can be damaged by load changes that shorten life through delamination and micro-cracking. Application of SOFCs in load following applications is discussed to improve lifecycle costs.

</td></tr><tr><td>Aerial Vehicle Control for Drogue Following
</td><td><a href='/wiki/uploads/Main/apm_uav_17Jan12.pdf'>Presentation</a>
</td><td>January 17, 2012
</td><td>Solomon Sun
</td><td>Path optimization of an Unmanned Aerial Vehicle (UAV) becomes more challenging with select constraints. Application of a UAV application is discussed from the MAGICC Lab group at BYU.

</td></tr><tr><td>Aerial Vehicle Control for Drogue Following
</td><td><a href='/wiki/uploads/Main/apm_uav_17Jan12.pdf'>Presentation</a>
</td><td>January 17, 2012
</td><td>Solomon Sun
</td><td>Path optimization of an Unmanned Aerial Vehicle (UAV) becomes more challenging with select constraints. Application of a UAV application is discussed from the MAGICC Lab group at BYU.

</td></tr><tr><td>Solid Oxide Fuel Cell Modeling and Control
</td><td><a href='/wiki/uploads/Main/apm_sofc_24Jan12.pdf'>Presentation</a>
</td><td>January 24, 2012
</td><td>Lee Jacobsen
</td><td>Solid Oxide Fuel Cells (SOFCs) can be damaged by load changes that shorten life through delamination and micro-cracking. Application of SOFCs in load following applications is discussed to improve lifecycle costs.

</td></tr><tr><td>Nonlinear Programming with APM MATLAB
</td><td><a href='/wiki/uploads/Main/apm_tutorial_21Feb12.pdf'>Presentation</a>
</td><td>February 21, 2012
</td><td>John Hedengren
</td><td>The APM interface extends MATLAB to be used for parameter estimation, nonlinear control, and optimization. Participants can <a href='/wiki/uploads/Main/apm_demo.zip'>download a few example applications</a> to run prior to the meeting that will be covered as part of the tutorial.

</td></tr><tr><td>Optimal Identification for Model Predictive Control
</td><td><a href='/wiki/uploads/Main/apm_darby_13Mar12.pdf'>Presentation</a>
</td><td>March 13, 2012
</td><td>Mark Darby
</td><td>To identify models for MPC applications in chemical plants and refineries, the process is perturbed through increasingly automated step testing. Mark will give a tutorial overview of how to apply practical control engineering and design experiments for model development of dynamic systems.

APM User's Group Webinars are held every two-weeks at 10 AM Mountain Time / 11 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

to:

APM User's Group Webinars are held about every two-weeks at 10 AM Mountain Time / 11 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

to:

</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

</td><td>David Grigsby and Casey Abbott
</td><td>The <a href='https://sbml.org'>Systems Biology Markup Language (SBML)</a> is a repository of hundreds of dynamic models in computational biology. This presentation will provide a tutorial on using SBML models for dynamic simulation, parameter estimation, and optimization.

</td><td>The equations that simulate separation processes naturally involve higher index Differential and Algebraic Equations (DAEs). Other popular techniques for DAE simulation can often only handle up to index-1 DAEs. New techniques for large-scale DAEs allow efficient solution for bifurcation analysis, simulation of complex systems, and optimization.

to:

</td><td>The equations that simulate separation processes naturally involve higher index Differential and Algebraic Equations (DAEs). Other popular techniques for DAE simulation can often only handle up to index-1 DAEs. New techniques for large-scale DAEs allow efficient solution for bifurcation analysis, simulation of complex systems, and stability analysis.

</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (10 Hz) without storage and retrieval for a four tank process.

to:

</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

</td><td>Isak Nielsen
</td><td>Empirical models obtained through subspace identification are a popular form for model predictive control (MPC) applications. This presentation relates a practical application of linear MPC with models obtained by regression techniques. Methods for sparse discrete or continuous Linear Time Invariant (LTI) forms in APMonitor are also reviewed.

</td><td>Jose Mojica
</td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers. <a href='/wiki/uploads/Main/apm_mpc_demo.zip'>Download example application</a>

</td></tr><tr><td>Optimal Boiler Control
</td><td><a href='/wiki/uploads/Main/apm_boilers_3Mar12.pdf'>Presentation</a>
</td><td>March 20, 2012
</td><td nowrap>10AM MST
</td><td>Jose Mojica
</td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers. <a href='/wiki/uploads/Main/apm_mpc_demo.zip'>Download example application</a>

To participate in an upcoming presentation, click register and provide your name and e-mail address. Log-on information is used to communicate log-on information and a reminder notice just prior to the meeting. There is no charge to register for a webinar. Users can participate via audio or video conference through a telephone or internet connection.

to:

To participate in an upcoming presentation, click register and provide your name and e-mail address. Registration is used to communicate log-on information and a reminder notice just prior to the meeting. There is no charge to register for a webinar. Users can participate via audio or video conference through a telephone or internet connection.

</td><td>Traditionally, Model Predictive Control (MPC) is executed at a rate of 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (10 Hz) without storage and retrieval for a four tank process.

to:

</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (10 Hz) without storage and retrieval for a four tank process.

</td></tr><tr><td>Fast Model Predictive Control
</td><td>
</td><td>April 24, 2012
</td><td nowrap>10AM MST
</td><td>Trevor Slade
</td><td>Traditionally, Model Predictive Control (MPC) is executed at a rate of 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (10 Hz) without storage and retrieval for a four tank process.

APM User's Group Webinars are held every two-weeks at 8 AM Mountain Time / 9 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

to:

APM User's Group Webinars are held every two-weeks at 10 AM Mountain Time / 11 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

</td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers.

to:

</td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers. <a href='/wiki/uploads/Main/apm_mpc_demo.zip'>Download example application</a>

Past Presentations and Future Schedule

To participate in an upcoming presentation, click register and provide your name and e-mail address. Log-on information is used to communicate log-on information and a reminder notice just prior to the meeting. There is no charge to register for a webinar. Users can participate via audio or video conference through a telephone or internet connection.

</td></tr><tr><td>Optimal Boiler Control
</td><td><a href='https://byu.webex.com/byu/j.php?ED=190523552&RG=1&UID=0&RT=MiM2 '>Register</a>
</td><td>March 20, 2012
</td><td nowrap>10AM MST
</td><td>Jose Mojica
</td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers.

</td></tr><tr><td>Linear Model Predictive Control
</td><td><a href='https://byu.webex.com/byu/j.php?ED=193601947&RG=1&UID=0&RT=MiM2 '>Register</a>
</td><td>April 3, 2012
</td><td nowrap>10AM MST
</td><td>Isak Nielsen and John Hedengren
</td><td>Empirical models obtained through subspace identification are a popular form for model predictive control (MPC) applications. This presentation relates a practical application of linear MPC with models obtained by regression techniques. Methods for sparse discrete or continuous Linear Time Invariant (LTI) forms in APMonitor are also reviewed.

</td><td>Large scale biological models are important to gain an understanding of complex pathways to improve treatments such as anti-viral application. Simulated clinical trial data is aligned to an available model of HIV infection.

To participate in an upcoming presentation, click register and provide your name and e-mail address. Log-on information is used to communicate log-on information and a reminder notice just prior to the meeting. There is no charge to register for a webinar. Users can participate via audio or video conference through a telephone or internet connection.

</td><td>Kody Powell, UT Austin
</td><td>Dynamic thermal energy storage with weather forecasting has the potential to improve solar energy capture by up to 64%. Results from a recent study are discussed to demonstrate the benefits of dynamic optimization.

</td><td>Dustin Marshall
</td><td>Friction Stir Welding (FSW) temperature control is important to achieve uniform and consistent weld properties. This application is using a PDAE model for estimation and control. The model validation, control, and experimental data testing are discussed.

</td><td>Casey Abbott
</td><td>Large scale biological models are important to gain an understanding of complex pathways to improve treatments such as anti-viral application. Simulated clinical trial data is aligned to an available model of HIV infection.

</td></tr><tr><td>Optimal Boiler Control
</td><td><a href='https://byu.webex.com/byu/j.php?ED=190523552&RG=1&UID=0&RT=MiM2 '>Register</a>
</td><td>March 20, 2012
</td><td nowrap>10AM MST
</td><td>Jose Mojica
</td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers.

</td></tr><tr><td>Linear Model Predictive Control
</td><td><a href='https://byu.webex.com/byu/j.php?ED=193601947&RG=1&UID=0&RT=MiM2 '>Register</a>
</td><td>April 3, 2012
</td><td nowrap>10AM MST
</td><td>Isak Nielsen and John Hedengren
</td><td>Empirical models obtained through subspace identification are a popular form for model predictive control (MPC) applications. This presentation relates a practical application of linear MPC with models obtained by regression techniques. Methods for sparse discrete or continuous Linear Time Invariant (LTI) forms in APMonitor are also reviewed.

Webinars on Optimization Applications

APM User's Group Webinars are held every two-weeks at 8 AM Mountain Time / 9 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

Past Presentations and Future Schedule

To participate in an upcoming presentation, click register and provide your name and e-mail address. Log-on information is used to communicate log-on information and a reminder notice just prior to the meeting. There is no charge to register for a webinar. Users can participate via audio or video conference through a telephone or internet connection.

</td></tr><tr><td>Nonlinear Programming with APM MATLAB
</td><td><a href='/wiki/uploads/Main/apm_tutorial_21Feb12.pdf'>Presentation</a>
</td><td>February 21, 2012
</td><td nowrap>8AM MST
</td><td>John Hedengren
</td><td>The APM interface extends MATLAB to be used for parameter estimation, nonlinear control, and optimization. Participants can <a href='/wiki/uploads/Main/apm_demo.zip'>download a few example applications</a> to run prior to the meeting that will be covered as part of the tutorial.

</td></tr><tr><td>Optimal Boiler Control
</td><td><a href='https://byu.webex.com/byu/j.php?ED=190523552&RG=1&UID=0&RT=MiM2 '>Register</a>
</td><td>March 20, 2012
</td><td nowrap>10AM MST
</td><td>Jose Mojica
</td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers.

</td></tr><tr><td>Linear Model Predictive Control
</td><td><a href='https://byu.webex.com/byu/j.php?ED=193601947&RG=1&UID=0&RT=MiM2 '>Register</a>
</td><td>April 3, 2012
</td><td nowrap>10AM MST
</td><td>Isak Nielsen and John Hedengren
</td><td>Empirical models obtained through subspace identification are a popular form for model predictive control (MPC) applications. This presentation relates a practical application of linear MPC with models obtained by regression techniques. Methods for sparse discrete or continuous Linear Time Invariant (LTI) forms in APMonitor are also reviewed.